Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Genetic Programming

  • Moshe Sipper
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_340

Genetic Programming is a subclass of  evolutionary algorithms, wherein a population of individual programs is evolved. The main mechanism behind genetic programming is that of a generic algorithm, namely, the repeated cycling through four operations applied to the entire population: evaluate–select–crossover–mutate. Starting with an initial population of randomly generated programs, each individual is evaluated in the domain environment and assigned a fitness value representing how well the individual solves the problem at hand. Being randomly generated, the first-generation individuals usually exhibit poor performance. However, some individuals are better than others, that is, as in nature, variability exists, and through the mechanism of selection, these have a higher probability of being selected to parent the next generation. The size of the population is finite and usually constant.

See  Evolutionary Games for a more detailed explanation of genetic programming.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Moshe Sipper
    • 1
  1. 1.Ben-Gurion UniversityBeer-ShevaIsrael